Growth of Different Shape Au Nanoparticles through an Interfacial Redox Process Using a Conducting Polymer

Langmuir ◽  
2010 ◽  
Vol 26 (4) ◽  
pp. 2785-2790 ◽  
Author(s):  
Pratap Mukherjee ◽  
Arun K. Nandi
Keyword(s):  
Conducting Polymer ◽  
Au Nanoparticles ◽  
Redox Process

Detection of daunomycin using phosphatidylserine and aptamer co-immobilized on Au nanoparticles deposited conducting polymer

2011 ◽  
Vol 26 (11) ◽  
pp. 4442-4449 ◽  
Author(s):  
Pranjal Chandra ◽  
Hui-Bog Noh ◽  
Mi-Sook Won ◽  
Yoon-Bo Shim
Keyword(s):  
Conducting Polymer ◽  
Au Nanoparticles

Polymer to Electrode Adhesion Enhancement Based on Novel PI/Au (Nanolayer)/Polypyrrole Three-Bending-Beam Actuator Fabrication

2012 ◽  
Vol 622-623 ◽  
pp. 556-560
Author(s):  
Gholamreza Kiani ◽  
Mojtaba Shahi ◽  
Ali Rostami
Keyword(s):  
Chemical Synthesis ◽  
Scanning Tunneling Microscopy ◽  
Conducting Polymer ◽  
Redox Process ◽  
Scanning Tunneling ◽  
Synthesis Methods ◽  
Tunneling Microscopy ◽  
Electroactive Material ◽  
Polymer Actuator ◽  
Structure Strength

Novel combined electrochemical and chemical synthesis methods for the preparation of Polypyrrole-based actuators are presented. Polypyrrole (PPy) actuators were electrochemically synthesized and after coating with a thin gold nano-layer, prepared into a Polyethersulfone (PI) substrate. Scanning Tunneling Microscopy (STM) and a potentiostat–galvanostat were used to confirm the actuation of PPy based actuators during the redox process. Three-layer actuator based on polypyrrole as electroactive material, Polyethersulfone as substrate and gold nanolayer in our proposed method have been realized. The structure strength and layer adhesion have been improved. This advancement in conducting polymer actuator technology will impact many engineering fields, where a stable, lightweight and large displacement actuator is needed.

Fabrication of Conducting Polymer Micro/Nanostructures Coated with Au Nanoparticles for Electrochemical Sensors

2012 ◽  
Vol 12 (6) ◽  
pp. 4975-4978 ◽  
Author(s):  
Taechang An ◽  
WooSeok Choi ◽  
Eunjoo Lee ◽  
Seong J. Cho ◽  
Geunbae Lim
Keyword(s):  
Conducting Polymer ◽  
Au Nanoparticles ◽  
Electrochemical Sensors

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

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